Scale Python calculations with STK Parallel Computing Server 2.0

As the level of detail required for a calculation grows, so does the amount of time and memory needed to compute. STK Parallel Computing Server provides the development and runtime environment to distribute large-scale jobs, using multiple computing resources to process more at once. The server can use multiple cores on a single machine or across multiple machines on a network, and can be shared amongst multiple users. Previously, users could submit jobs to the server for parallel execution in a number of ways:

From a custom application that uses the STK Parallel Computing Server API for .NET or Java.

New in the STK Parallel Computing Server 2.0 release, the STK Parallel Computing Server API is now also available in Python. The software development kit includes the API, is fully documented, and provides multiple examples to help you get started parallelizing your own Python scripts. If you have existing STK or ODTK scripts written in Python, try adapting them to use this new API to take advantage of your existing hardware and speed them up.